Research Organization of Information and Systems

The Institute of
Statistical Mathematics
2010 -2011

CONTENTS■ Message from Director-General1■ Institute Overview

2■ Research Centers

4■ Research Cooperation

International Cooperation

16

Research Collaboration

16■ Graduate School Program17■ Outreach Activities

Tutorial Courses

20

Statistical Mathematics Seminar

21

Open Lecture

21

Consultation about Statistical Science 22
Annual Research Meeting for 200922

■ Facilities and Equipment

23■ Finance and Buildings24■ Organization25■ History

31

Message from Director-General
Message from Director-General

In October 2009, the Institute of Statistical Mathematics relocatedto a new campus in Tachikawa City, leaving the Minami-Azabu
area of Minato-ku which was our base for more than half acentury. In June 2010, we officially opened the Akaike Guest
House, giving us the facilities to host joint research projects
involving stays. The relocation fulfills a decision that was mademore than two decades ago in 1988. Every one of us is looking
forward to further building on our research, as we set out witha sense of renewed purpose to fulfill our vital mission as aninter-university research institute.
In April 2010, the Institute of Statistical Mathematics entered
its second medium-term period since being incorporated into
the Research Organization of Information and Systems, which
has launched its activities under a new medium-term plan andtargets. As a member of the Research Organization of Informationand Systems, we have set out to form a Network Of Excellence(NOE) for integrated risk science, survey science, nextgeneration simulation science, statistical machine learning, andservice science, in order to establish new methodologies inthese fields and serve as hubs for interdisciplinary interaction.
We have also launched a project to foster statistical thinking,recognizing that the development of human resources withstatistical thinking and research coordination skills is essential
for the future advancement of science in Japan, and of society.
Rapid advancements in information and communications
technologies are greatly transforming our society and scientificresearch. The use of large-scale data will play a key role in theadvancement of science and technology and in society moving
forward. There is an urgent need to establish a data-centricscience, which is regarded as a fourth scientific methodology.
The Institute of Statistical Mathematics will endeavor to meetthe needs of the times by further advancing our joint researchsystems and activities under the strategic research centers,
while securing the success of our two new projects. We lookforward to your continued understanding and support for ouractivities.

Genshiro Kitagawa
Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling

Institute Overview

The Department of Statistical Modeling conducts research on the modeling of causally, temporally and/or
spatially interrelated complex phenomena, including intelligent information processing systems. It also conducts
researches on model-based statistical inference methodologies.

䡵 Spatial and Time Series Modeling GroupThe Spatial and Time Series Modeling Group works on modeling and inference for the statistical analysis of time series,
spatial, and space-time data, and on their applications to prediction and control.

䡵 Intelligent Information Processing GroupThe Intelligent Information Processing Group works on concepts and methods for the extraction, processing and transformation of information in intelligent systems, motivated by an active interest in practical problems in engineering and science.

䡵 Graph Modeling GroupThe Graph Modeling Group works on analyses of the data generated by systems with a graph structure and on the modeling required in order to reconstruct the original system.

Department of Data Science
The Department of Data Science aims to develop research methods for surveys, multidimensional data
analyses, and computational statistics.

䡵 Survey Research GroupThe Survey Research Group focuses on research related to statistical data collection and data analyses.

䡵 Multidimensional Data Analysis Group

The Multidimensional Data Analysis Group studies methods for analyzing phenomena grasped on multidimensional
space and ways for collecting multidimensional data.

䡵 Computational Statistics GroupThe Computational Statistics Group studies sophisticated uses of computers in statistical methodology such as
computer-intensive data analyses, computational scientific methods and statistical systems.

Department of Mathematical Analysis and Statistical Inference
The Department of Mathematical Analysis and Statistical Inference carries out research into general statisticaltheory, statistical learning theory, optimization theory, and the practice of statistics in science.

䡵 Mathematical Statistics Group

The Mathematical Statistics Group is concerned with aspects of statistical theory and probability theory that have statistical applications.

䡵 Learning and Inference Group

The Learning and Inference Group develops statistical methodologies that enable researchers to learn from data sets andto properly extract information through appropriate inference procedures.

䡵 Computational Mathematics GroupThe Computational Mathematics Group studies computational algorithms together with mathematical methodologies used
for statistical modeling in the sciences.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists the development of statistical science by managing
the computer systems used for statistical computing, facilitating public outreach, and supporting the research
activities of both staff and collaborators.

䡵 Computing Facilities Unit The Computing Facilities Unit is in charge of managing computer facilities, software forresearch, networking infrastructure and network security.

䡵 Information Resources Unit The Information Resources Unit is responsible for maintaining an extensive library and an electronic
repository, and is in charge of planning statistical education courses to popularize research results.

䡵 Media Development Unit The Media Development Unit is in charge of the publication and editing of research results
and is responsible for public relations.

2Strategic ResearchPrediction and Knowledge Discovery Research Center

Institute Overview

The Prediction and Knowledge Discovery Research Center studies the statistical modeling and inference algorithms
that can be used to extract useful information from the huge amount of data which complex systems produce, and thus
attempts to solve real-world problems in many different scientific domains, especially genomics, earth and space sciences.

䡵 Molecular Evolution Research Group

The Molecular Evolution Research Group researches the area of molecular phylogenetics, and seeks to develop statisticalmethods for inferring evolutionary trees of life using DNA and protein sequences.

䡵 Data Assimilation Research GroupThe Data Assimilation Research Group aims at developing new, advanced data assimilation techniques to combine
different information from dynamical simulation and observation data.

䡵 Statistical Seismology Research GroupThe Statistical Seismology Research Group is concerned with the evaluation of seismicity anomalies, detection of crustal
stress changes, their modeling, and the probability forecasting of large aftershocks and earthquakes.

䡵 Statistical Genome Diversity Research GroupThe Statistical Genome Diversity Research Group aims to construct novel methodologies for learning and inference from avariety of data sets in the rapidly growing area of bioinformatics.

Risk Analysis Research Center
The Risk Analysis Research Center is pursuing a scientific approach to the study of the increased uncertainty
and risk associated with the increasing globalization of the society and economy. The center is also constructing a network for risk analysis in order to contribute to the creation of a reliable and safe society.

䡵 Food and Drug Safety Research GroupThe Food and Drug Safety Research Group aims to develop the statistical framework and methodology of quantitativerisk evaluation for substances ingested by the human body.

䡵 Environmental Risk Research GroupThe Environmental Risk Research Group studies the statistical methodologies related to environmental risk and environmental monitoring.

䡵 Financial Risk and Insurance Research GroupThe Financial Risk and Insurance Research Group explores the use of statistical modeling methods to quantify the risks
involved with financial instruments and insurance products.

䡵 Research Group for Reliability and Quality Assurance of Service and Product

The Research Group for Reliability and Quality Assurance of Service and Product aims to increase the safety of products
and services by developing statistical methods that contribute to quality assurance and reliability and by promoting the
adoption of these methods in the industrial world.

Research Innovation Center
The objective of this center is to establish innovative research in statistical mathematics to keep up with new
trends in the academic and real world. The center carries out original research projects, ranging over bothpure and applied frontiers.

䡵 Social Survey Information Research GroupThe Social Survey Information Research Group collects several social survey results with the goal of assembling them todevelop a statistical methodology to describe the social world.

䡵 Functional Analytic Inference Research Group

This group aims to develop nonparametric methodology for statistical inference using reproducing kernel Hilbert spaces
given by positive definite kernels, and to apply these techniques to causal inference problems.

䡵 Advanced Monte Carlo Algorithm Research GroupThe Advanced Monte Carlo Algorithm Research Group aims to develop Markov Chain Monte Carlo and Sequential Monte
Carlo algorithms and study their applications.

䡵 Random Number Research Group

This group carries out research into random number generation, including hardware random number generators, andtesting randomness with methods such as time series analysis.

䡵 Speech and Music Information Research GroupThe Speech and Music Information Research Group investigates novel information retrieval methods using machinelearning from time series data, including speech, music, and brain data.

䡵 Optimization-based Inference Research Group

Optimization-based Inference Research Group focuses on optimization methodology as a fundamental tool for computational inference and aims to develop new inference techniques in statistical applications.

3Research Centers
Prediction and Knowledge Discovery Research Center

Molecular Evolution Research Group

Molecular Evolution and Biodiversity

䡵 Mammalian evolution andcontinental drift
We are studying such problems inherent
in phylogenetic analyses and are investigating new methods to overcome suchproblems during the process of solving
real biological problems, including mammalian evolution and biodiversity of Madagascar. Recent molecular phylogenetics
has clarified that Placentalia (eutherianmammals) consists of three groups; i.e.,
Boreotheria originally from Laurasia,
Afrotheria from Africa, and Xenarthra fromSouth America. These groupings are
considered to reflect continental separation around 100 MyrBP, but the root of thetree remains ambiguous. We are studying
this rooting problem by using genomescale data.
Jun Adachi

Primatomorpha

Euar chonta

Euarchontoglires

Eutherian Tree
Primates

Dermoptera(Colugo)

Scandentia(Tupai)
Lagomorpha

GliresRodentia

Boreotheria

Cetartiodactyla

Pegas oferae

Laurasiatheria

Perissodactyla

Carnivora
Pholidota（pangolin）
Chiroptera

Eulipotyphla(hedgehog,mole)

Xenarthra（anteater,armadillo,sloth）
Afrosoricida(tenrec,
golden mole）
Afrotheria

Research Centers䡵 Molecular phylogeny
The aim of this project is to understandthe biodiversity on Earth from various view
points from the molecular level to theecological level. An evolutionary view is
indispensable in the integrative understanding of the biodiversity, and the methods for inferring molecular phylogeny are
essentially important. In this project, weare developing models of nucleotide andamino acid substitutions during evolution
and methods for phylogenetic inference
based on maximum likelihood. It is notnecessarily easy to know the phylogenetic
tree correctly even if genome-scale data
become available, because a wrong tree
may be supported strongly in the presenceof bias in inferring the tree.

Macroscelidea(elephant shrew）
Tubulidentata(aardvark)
Proboscidea・Sirenia
・Hyracoidea

Continental configuration of 100 Myr BP
and Eutherian evolution
Boreotheria

Afrotheria
Xenarthra

4

Data Assimilation Research Group

Data Assimilation: Time-dependent Information Fusion from Numerical
Simulation and Large-scale Observation
our environment. We are therefore constructing
new computation methods based on the sequential DA methods and conducting five DA projects.
One is done with the ensemble Kalman filter that
assimilates the TOPEX/Poseidon altimetry to thecoupled ocean-atmosphere simulation model.
The second DA is done with a particle filter for
Tsunami simulation model to correct bottom
topography. The third DA project is aimed at finding an uncertainty in sea bottom topography
which plays an important role in conducting an
ocean tide simulation. The fourth is the DA project
to estimate distributions of ring current ions andelectric potential in the inner magnetosphere by
assimilating the series of the ENA data obtainedby the HENA imager on board of IMAGE satellite
into a kinetic ring current model (CRCM). The last
project is to apply the DA methodology to combine
a simulation model with observed data like microarray gene expression data for understanding biological pathways.
Tomoyuki Higuchi

Research Centers

Data Assimilation (DA) is a technique for a synthesis of information from a dynamic (numerical)
model and observation data. It is an emerging
area in earth sciences, particularly oceanography,
stimulated by recent improvements in computational and modeling capabilities and the increasein the amount of available observations. In statistical methodology, DA can be formulated in thegeneralized state space model, where the systemand observation model correspond to large-scale
numerical model-based simulations and large-scalesatellite- and/or ground-based measurement systems, respectively. Past studies for DA employed alinear Gaussian state space model and applied
Kalman filter. The Kalman filter based methods,
however, do not allow for the strong nonlinear
and/or non-Gaussian disturbance behaviors. Many
phenomena in earth sciences tend to be discussed
in terms of a complex system in which the nonlinear non-Gaussian fluctuations (disturbances) play
an important role. The nonlinear non-Gaussian
DA method needs to be developed in an attempt
to improve a performance of prediction ability of

biological pathway models

Data
Assimilation
Tuning
SBML/CSML
plug-in

LiSDAS

Tuningrelease of model libraries

Order-made & Online Consultation
Contribution to drug discovery

structure
extraction
model
updateNetwork structure
search programplug-in & release

Figure: Schematic illustration that explains aims and objects of our research project:
Development of the customized simulation model based on the data assimilation technology.

5Prediction and Knowledge Discovery Research Center

Statistical Seismology Research GroupSpace-time Heterogeneity in Aftershock Activity
a large aftershock that triggers significantly many
secondary aftershocks. We examined whether theaftershock shadow and activated zone agree withthe region of Coulomb-failure-stress (CFS) decrease(stress shadow) or increase, respectively, by assuming an aseismic slip on or down-dip fault of the
focal large aftershock or elsewhere. As a result,for each case, we have found a plausible slip thatappears to produce stress shadow and increased
zone that overlaps the aftershock shadow or activated zone. In order to explain the transient anomalies, we have considered six simulation scenarios
using the rate/state-dependent friction law of Dieterich taking account of the fault mechanisms of themainshocks and large aftershocks. Some aseismic
slips revealed recently by GPS observations support
the presently discussed space-time anomalies inaftershock activity.

Research Centers

For a number of aftershock sequences in andaround Japan, we examine de-trended space-timecoordinates after fitting the Omori-Utsu occurrence
rate to each aftershock sequence. Although the
Omori-Utsu decay rate well predicts the trend of
mass frequency of whole aftershocks in time, local
aftershock occurrences violate the typical OmoriUtsu law more or less, which implies that seismic
stress change takes place locally. We investigated
how the aftershock occurrences in a local subregion deviate from the space-time Omori-Utsu
function. Space-time coordinates regarding thefrequency-linearized time (FL-time) should appear
temporally uniform everywhere if the aftershock
activity lasts normally.
We are concerned with each of the aftershock
shadows and the activated zone. These appear not
only post-seismically after the mainshock or aftera large aftershock but also pre-seismically precede141.6 °

References: Ogata, Y. (2010). Space-time heterogeneity inaftershock activity, Geophys. J. Int, 181,1575-1592,
doi: 10.1111/j.1365-246X.2010.04542.x.

141.7 °

(a)

(b)

38.95

Yosihiko Ogata

3.5

3.0

LATITUDE

38.9
38.9 °

38.85

2.5

Figure 1:
Aftershock epicenters of the 2003 Sanriku
southern coast earthquake (a ) and de-trended
space-time plot (b).

38.8
38.8 °

2.0
38.75
1.5
0

0.1

0.5 1

5

10

20

40

80

LONGITUDE
136.8

3.2

3.4

(a)

1
2

TIME (days)

0.5
4

37.0 °

6
8
12
3.6

3.0

2.8

2.6

6

136.7

(b)

0.1

Figure 2:
Aftershock epicenters (a) of 2007 Noto Peninsula
earthquake and de-trended space-time plot ( b).
In addition, panel (a) shows the CFS increments,
at the depth of 10 km, where contours of red
and blue show positive and negative values,
respectively. Here we assume that the majorityof the primary aftershocks have the similarangles to the mainshock’s. We also assume thestress transfers from a slip preceding the largest
aftershock (blue circle in panel b) with the fault
model (small rectangle in panel a).

136.6

0

136.5 °
37.5 °

136.5

136.4

TIME (days)

Research Centers

Statistical Genome Diversity Research Group

Two Approaches for Gene Association Study–– 2-sample Test and Machine Learning ––the early stopping rule, and furthermore regularization is added in any stage of the leaningalgorithm. In SVM the recursive feature elimination
algorithm is widely accepted. Basically LASSO
employs L1 regularization for sparse leaning, inwhich the original propose is now evolved to
MARS, Dantzig selector and other variants. In theseyears there are published a number of papers
that investigates statistical performance including reconciliation between boosting and LASSO(stagewise LASSO). In this way this approach
challenges the essential difficulty of variable selection. However, the weak point for overlearning
frequently loses the efficient performance. Any
approach from machine leaning does not attainthe final solution for the problem, p >> n.

Research Centers䡵 2-sample test
Our research group discusses statistical aspects
to solve ‘p >> n’ Problem which is caused byhuman genome diversity. For example, we challengea problem to predict a phenotype based on gene
expression data sets with p gene expressions forn subjects. Typically the phenotype expresses
treatment effects, in which an efficient prediction
enables to enhancement for personalized medicine. In effect, the prediction for treatment effectbased on the measurements for gene expressionsof subjects before treatment contributes to fulfillment of personalized healthcare. Presently, there
are totally different two approaches. One is tofocus on only one signal of gene expression, andtest a hypothesis from 2 samples stratified by thebinary phenotype. This is reduced by a classical2-sample test in which, for example, we employ
t-statistic or Wilcoxon statistic for the group withtreatment effect and the group with no treatment
effect. In statistical community there actively
discussed several methods to do testing hypothesis
controlling false discovery rates. This approach isadvantageous over others in the light of confirmatory paradigm. However, the power of prediction
is not so expected because it is based only on theinformation from a single gene.

䡵 2-sample test and machine learning
Our research group discusses a unified approachcombining these two approaches. To begin withthis we do not get a set of t-values for p single
variable analyses, but directly get t-value for a
prediction score. In this way we consider t-value
for a general prediction score F(x), in which we findthe prediction score that has a maximum of t-value
in a set of all prediction scores in a booting learning algorithm, called t-Boost. This study has justbeen started, so that it will provide a good prediction score to implement a personalized medicine.
Presently our group proceeds co-operative workwith research teams of National Cancer Centerand those of Mitsubishi Chemical Holdings.
Shinto Eguchi

䡵 Machine learning
The other approach is to predict the phenotype
by combining signals of several gene expressions.
In a field of machine learning, there are rapid
developments on boosting method, SVM (support
vector machine), LASSO(least absolute shrinkage
and selection operator)
Gosset (1908)
Student's t-test
and so on. The key is toselect informative genes
Wilcoxon’s rank statistics
Wilcoxon (1945)
in p genes for the prediction, however, the problem:
False Discovery Ratep >> n causes over-learning
Benjamini and Hochberg (1995)
of such methods before
Adaptive FDR
selecting the informative
Benjamini and Hochberg (2000)
genes. To overcome thisdifficult aspect, the boostMultiple Two-sample Test
ing method is modified by

LARS
Tibshirani (1996)

Efron et. al (2004)

LASSO
Stagewise Lasso

SVM

Zhao and Yu (2008)

Dantzig

L2Boosting

Candes and Tao (2007)

Machine Learning

Boosting two-sample tests
7

Risk Analysis Research Center

Food and Drug Safety Research Group

Benefit-risk Balance Evaluation of Food and Drug
䡵 Mission
Social responsibility of the food and drug safety
program in RARC is to design appropriate databases
and statistical methods for the benefit-risk balanceevaluation of food and drug.

Research Centers

䡵 Benefit/risk analysis of drugs basedon large-scale database
Various large-scale databases have been available
for the scientific benefit / risk evaluation and risk
management of post-marketing drugs in Europe
and North America. In Japan, however, such alarge-scale database does not exist. As the first stepto improve this situation, we are building original
databases using data collected from pre-marketingclinical trials and post-marketing surveillance,
and we conduct benefit / risk analyses of drug effect.
䡵 Genome-wide association study between
phenotype and large-scale SNPs data
We study the relationship between effectivenessof treatment for hypertension and about 500,000
SNPs genotype data to find candidate genes forthe good selection of drug for hypertension. Aftera separation of training group or test group fromthe sample, we examine a genome-wide association
test for training group. Then it is gotten Figure 1
shows the loci and LD (D' ) map with chromosome
position and LD blocks on chromosome 2. LD maps
have been made using Haploview (Barrett et al.,2005) that is a very popular program in the statistical genetic world. For each LD block, we estimate
a penetrance and test the likelihood ratio for SNPs
and haplotypes to obtain candidate genes. If resultsof candidate genes are reproduced in the test
group, it may be useful information for good selection of antihypertensive drug.
䡵 Ad hoc study on a specific drug safety issue
An issue of drug safety often becomes a social
concern, and the immediate scientific elucidationis required. An appropriate study design, implementation and statistical analysis are necessary
to get an accurate scientific quantitative answer.
We participate in an ad hoc study on a specificdrug safety issue as experts of statistical scienceand epidemiology.
Toshiharu Fujita

Figure 1: Ldmap(D’) and LD blocks on Chr.2 by Haploview

8Research Centers

Environmental Risk Research GroupThe Effort of the Statistical Science towards Solutionof Environmental Problems

䡵 Long-term variation of water quality in Tokyo Bay
Although the pollution load flowing into Tokyo
Bay has been reduced, red tide still breaks out
frequently. Red tide increases the amount of oxygen
deficient water mass, and this sometimes causes
blue tide. The occurrence of red and blue tides
seems to be connected with elevated water temperature and change in salinity of recent date in Tokyo

Photo 1: Red tide

Photo 2: Blue tide

( The Tokyo Metropolitan ResearchInstitute for Environmental Protection)( Chiba Prefectural Environmental
Research Center )Photo 3: Growing experiment of cucumbers

Fig.1: Relation between
concentrations of Dieldrin( Niigata Agricultural Research Institute )Photo 4: Scheme to avoid Windstorm
in Denmark

Photo 5：Typhoon damage
in Ashoro, Hokkaido

Bay. This study is making a collection of water
quality data of Tokyo Bay and is analyzing thesedata to reveal long-term variation of water quality.
(photo 1, photo 2)
䡵 On persistent organic pollutants
To identify the sources of pollution by persistentorganic pollutants (POPs), this study is developing
statistical methods to make inference on pollution
sources besides organizing POPs data. Further,
this study is making contribution to the development of technology to reduce the risk of POPs invegetable farming. (photo 3, Fig 1)

Research Centers䡵 Mission
A risk analysis research center / environmental
risk research section aims at performing the contribution towards solution of the environmental
problem which is a modern subject by developing
the optimal new statistical methodology for eachenvironmental problem (for example, the problem
of dioxin, the problem of global warming, theproblem of continuous use of safe water). Moreover, in order to realize this purpose, in cooperation with the community of environmental science,research is carried out including a visiting teacher
or a project researcher.

䡵 Risk-hedge oriented optimization systemagainst natural and managerial disaster towardsustainable forest resource management
After the Kyoto protocol entered into force,
reduction of carbon dioxide in the atmosphere is
promised to slow down the degree of global warming. Among the proposed mechanisms under the
Kyoto protocol, forest resources are regarded asa source of carbon sequestration, where forestation, deforestation and reforestation are the main
human-induced activities. Housing and furniture
manufacturing is also contributing to carbon mitigation indirectly. Despite of these facts, unfavorable
current economic situation for forestry businessdiscourages forest owners to abandon forestry
practice in mountainous regions. Besides, typhoon
disaster and snow damage on forest stands is accelerating abandoning phenomena. In our research
project, we construct a risk-hedge oriented optimization system toward sustainable forest resource
management. In the system, we try to predict
events of natural disaster and managerial disasterspatially and temporally, and control themanagement scheme to avoid these disasters with a viewof these phenomena. (photo 4, photo 5,Fig 2)
Koji Kanefuji

Fig.2：Experiment for wind tolerance（Provided by Dr. H. Ishikawa, Tokyo Univ. of Sci.）

9

Risk Analysis Research Center

Financial Risk and Insurance Research Group

Assessing Credit Risk via Securitized Products

Research Centers

䡵 Aim of our group
Financial Risk and Insurance Research Group
mainly concerns the theory and practice in thequantitative analysis of risks involved with various
financial instruments and insurance products
from the view point of statistical modeling. Various
research projects are under way not only by ISM
researchers but by visiting and post doctoral
researchers. This article introduces a cooperative
research by Dr. Masakazu Ando (Project Researcher),Dr. Hiroshi Tsuda (Visiting Professor), Dr. Yoko
Tanokura (Project Researcher), Dr. Seisho Sato
(Associate Professor) and Dr. Genshiro Kitagawa(Director General).

䡵 Valuation of securitized papers by Copula
There are two mainstreams in modeling credit
risk of financial products, one is structural
approach, and the other is reduced form approach.
The latter is preferred by many practitioners
because of its flexibility and facility to include
corporate information related to credit risk.
Copula is a popular choice among the reduced
form approach. Primarily we are concerned withdefault correlation. The greatest advantage ofusing copula is that we can configure the joint
distribution of risk factors based on univariate
marginal distributions of the occurrence time ofvarious default events. On the other hand, we
have to specify in advance the probability distribution of default factors
as well as the correlation structure ofthem. Moreover, the most commonlyused copula (Gaussian) often does notdescribe the actual price movementof securitized papers.

Figure 1: Tranche-wise spread time series of Collateralized Debt Obligation ( CDO, left
vertical axis) and Credit Default Swap (CDS ) Index, right vertical axis. Rise of
spreads after the summer of 2007 suggests the heightening of credit risk.

䡵 Dynamic implied Copula approach
These problems have brought a newresearch direction where, without
assuming specific probability distribution, the distribution of the default
event occurrence should be implied by
the actual traded papers. This enables
us to estimate the hazard rates anddefault correlation that conform to themarket price. We extend this approach(so-called implied copula approach) toconsider the time transition of hazard
rate scenarios, hence introduced
dynamic implied copula approach. Now
that forecasting future hazard scenarios
are available, the risk management
based on this model can incorporate
the projection of future securitizedpapers.
Yoshinori Kawasaki

Figure 2:
Estimated probability distributions of hazard rate scenarios
from the price of CDO papers in US market. Right-handside in hazard scenario axis corresponds to heightened
credit risk case. The probability of high risk scenario ismushrooming as time approaches to the summer of 2008.

10

Research Centers

Research Group for Reliability and Quality Assurance of Service and ProductResearch for Quality Assurance of Product and Service
䡵 Outline of research䡵 Systems of parameter design andnew approach to the analysis of SN ratio
In SQC, based on experimental data on the quality characteristics of materials, mechanical equipment, products andthe like, techniques such as the QC tools, Fisher’s design ofexperiments method and regression analysis are used toascertain the current situation, investigate the causes andanalyze the important factors. On the other hand, Taguchi
methods treat the causes of variation in the customer usage
conditions, environmental conditions and the like as noise
factors, and aim to discover the conditions that make theeffects of these factors as small as possible. In parameter
design, inevitable errors resulting from noise factors arenormally larger than the random errors dealt with by SQC,
so more effort is put into the descriptive statistics approachrather than into postulating probability distributions.

Figure 1: Optimal CAE anti-vibration design approach for door outer mirrors

However, it is possible to take a statistical approach byintroducing noise factors into the model as parameterfactors, and considering the probability distribution of SN
ratios as a statistical quantity, thereby enabling a discussionof statistical inference.

䡵 Improvement of MT system by kernel method
In the discrimination of non-defective products fromdefective in the field of quality control, non-defective isassumed to be homogeneous group, from request to meetthe specification. On the other hand, defectives is notconsidered to be samples from specific population because
defectives occurred from various causes.
The discrimination to this type data is called nonsymmetric discrimination problem and MT (Mahalanobis
Taguchi) system is the representative non-symmetricdiscrimination method developed by Dr. Genichi Taguchi.
MT system discriminates based on mahalanobis distancefrom unit space which is assumed to be homogeneous group.
While kernel method is recently noted in machine learning
field and it can construct flexible decision boundary
through mapping data to high-dimensional feature space.
In this research, we approached from machine learning
method and proposed two methods. One is the nonsymmetric discrimination method where One-class SVM isapplied to unit space.

Research Centers

Quality engineering (Taguchi methods) is a technicalmethodology devised more or less independently by Genichi
Taguchi over half a century for the purpose of making
systematic improvements to quality. A key technique of
Taguchi methods is parameter design, which is known to
reduce the “variability” of characteristics at the productdesign and development stages, and there have been many
reports of successes in manufacturing industries such as
the automotive and electrical industries as part of theestablishment of Quality-Japan.
But although the development of theoretical methodologies for parameter design in Taguchi methods is being
actively pursued overseas as an extension of statisticalquality control (SQC) or statistical science, in Japan it has
only been researched to a limited extent. Therefore thisstudy aims to devise a statistical methodology for quality
engineering by clarifying the points of similarity between thedesign of experiments according to Fisher and Taguchi.

䡵 Effectiveness of statistical simulationin development and design
The advancement of development and design processes
through the effective utilization of statistical simulation isvital in achieving guaranteed high quality manufacturing.
Particularly in the field of automotive development anddesign, there is a need to change over from methods
involving evaluation based on the prototyping and testingof actual products to predictive evaluation-based methods
employing highly precise CAE (Computer-Aided
Engineering) analysis. Therefore, the author has
proposed and is demonstrating the effectiveness of ahighly precise CAE analysis approach utilizing SQC
Technical Methods (Moutain-Climbing Methods for
Problem Solving), which constitute the core principlesof Science SQC (New Quality Control Principle).
Figure 1 below illustrate working examples, with
figure 1 showing an optimal CAE anti-vibration design
approach for door outer mirrors [a1]. Such use ofstatistical simulation has enabled the practicalapplication of predictive evaluation-based approaches
by reducing the discrepancies in results obtained
from testing of actual products and CAE analysis to
one tenth (1-2%) of previous calculations (10-20%).
Toshihiko Kawamura
11Research Innovation Center

Social Survey Information Research Group

Building a Database of Social Survey Results

Research Centers䡵 Objective of the Social Survey Information
Research GroupThe Social Survey Information Research Group of
the Research Innovation Center has been collecting social survey results with the aim of building
a database of social surveys. A single survey
provides limited information, particularly in the
recent deteriorated survey environment. Multidimensional analysis using various types of social
surveys is essential for comprehending the complicated aspects of modern society. This researchgroup studies the methodologies for building a
database and develops statistical methods foranalyzing data. The group also intends to makethe database available for public use.
䡵 A comprehensive report of a survey
on the Japanese national character
The core data used to develop the database is
obtained from a survey on the Japanese national
character, which is conducted on a regular basisby the Institute of Statistical Mathematics. Thissurvey has been conducted every five years since
the first 1953 survey. The most recent was its twelfth

survey and was conducted in the autumn of 2008.
The questionnaire items in the survey concerned
the attitudes of the participants toward life, environment, religion, politics, race, the Japanese people,
and so forth. The results of this longitudinal survey,
which are crucial for evincing the transition ofJapanese character, are already available on theInternet. More comprehensive results, including
cross-tabulation tables and graphs, are also
provided on the Internet.
䡵 Database of related surveys
The survey on the Japanese national characteris by no means the only survey conducted by
the Institute. Various interrelated comparative
surveys and experimental surveys have also been
conducted. One of them is public opinion survey inTokyo conducted since 1953 to 1982. The resultsof these surveys are also included in the databaseand made accessible to the public one at a time.
The potential users of this database, including not
only researchers but also the general public, will
be able to analyze the data from various perspectives.
Takahiro Tsuchiya

Figure : Browsing survey results on the web

12Research Centers

Functional Analytic Inference Research Group

Kernel Method: Nonlinear Data Analysis with Efficient Computation

䡵 Nonparametric data analysis with positive
definite kernels
The kernel method was first developed as“kernelization” of existing linear methods: support
vector machine and kernel principal component
analysis are among such examples. More recently,xiΦ

however, it has been recognized that the kernel
method provides a new approach to nonparametric statistical inference: by the ability of representing probabilities with the mean of the feature map,the basic statistics such as mean and covarianceof the feature maps can derive various nonparametric methods of data analysis. Our group has
been making central contributions in this line ofresearch. By using characterization of conditional
independence with kernels, we have been applying
the methodology to various problems including
dimension reduction in regression, causal network
learning, and inference of nonlinear dynamics.
We are also carrying out projects on theoreticalfoundations of the kernel method and approximation of the large matrix computation.

Research Centers䡵 What is the kernel method ?
The kernel method is a recent methodology for
analyzing data having a complex structure. The
basic idea is to use a feature map of data into ahigh-dimensional functional space to extract thehigh order moments of the data, without pursuingcomplex modeling. The similar, traditional waysof extracting nonlinearity by power series expansion or basis function expansion have a problem ofcomputational explosion for high-dimension data.
The kernel methods avoid this problem by using
the clever device of “positive definite kernel”,
which computes the inner product of the highdimensional functional space by simply evaluating
the kernel values. This gives an efficient method
for incorporating high order moments of data. In
addition, another strong point of the kernel method
is that it provides a seamless methodology for
non-vectorial data such as network data andsymbol sequences like texts.

䡵 International research network
Our group has active collaboration networks withresearchers in the world, in particular, with top
researchers in Max Planck Institute (Germany),
University of California, Berkeley and San Diego,
and Carnegie Mellon University. We have given
tutorials in various international conferencessuch as Machine Learning Summer School 2007
(Germany) and International Conference on
Machine Learning 2008 (Finland), and have beenworking as one of theresearch centers of
this area.
H
Kenji Fukumizu

Feature map

xｊ

Data space

Feature space(functional space)

Figure 1: Feature map of kernel method

Sex

IncomeFigure 2: An example of causal network
learning by the kernel method

Outlook

AgeFinancial
Area
Political
13

Research Innovation Center

Advanced Monte Carlo Algorithm Research Group

Rare Event Sampling by Markov Chain Monte Carlo

Research Centers

Our research focuses on the development ofnovel and nonstandard applications of Markov
Chain Monte Carlo algorithm (MCMC). MCMC wasintroduced in physics in the 1950s by Metropolisand coworkers and proved to be a useful tool in
the field of Bayesian modeling in 1990s. MCMC
is, however, a general strategy for sampling multivariate distributions with unknown normalization
constants and can in principle be applied to manyother problems.
Here we discuss rare event sampling (i.e.,sampling from the tails of the given distribution)
problem as an example of the promising application fields of MCMC. (Fig.1) By using MCMC,
especially with its variants “Replica Exchange
Monte Carlo” and “Multicanonical Sampling”, we
can generate samples of very small probability,
say, 10 -15, or even 10 -100, under a given model, andcalculate its probability correctly. Today’s mostpowerful computer can never achieve such anultimate precision with naïve random sampling.

With this approach we can solve various interesting problems. For example, we can sample
very rare errors in an information processing
systems and access its probability. Also, we canfind tiny regular orbits in chaotic systems (Fig.2).
For network design problems, we can generategraphs called “expander” or “Ramanujan graph”(Fig.3), which have nice properties, e.g., efficient
communication and fast diffusion on them. An
advantage of MCMC is that it can generate not
only a sample that optimizes a given criterion,
but also a number of samples with optimal or
suboptimal properties; we can also calculate probabilities of obtaining objects with these properties.
Because our problems are highly crossdisciplinary, collaboration with specialists of eachfield is important. We are exploring frontiers of
MCMC with scientists at Univ. Tokyo, Hokkaido
Univ., Osaka Univ., and Kyoto Univ..
Yukito Iba

Figure 1: Rare event sampling.

Figure 2: A tiny torus found by multicanonical Monte Carlo;
the torus is embedded in a chaotic sea shown in
the inset panel.

* Fig.2 is reproduced by courtesy of
Mr. Akimasa Kitajima (Osaka University).

Figure 3: An example of Ramanujan graph found by
multicanonical Monte Carlo.

14

Fig.3 is reproduced by courtesy of
Mr. Nen Saito (Osaka University).

Research Centers

Random Number Research GroupA New Hardware Random Number Generator䡵 Random number
Random number is defined a sequence of randomvariables. We think binary random variables withequal probability. It is easy to understand that we
can generate this random numbers from a resultof finite number of honest coin tossing.

䡵 Hardware physical random number generator
Random numbers are used for Monte Carlo
method, MCMC, bootstrap and so on. Large-scale䡵 Hardware random number generation
At the Institute of Statistical Mathematics,“Supercomputer system for Statistical Science”
was replaced in January, 2010. New hardware
random number generation system is shown in
Fig. 1. Generation speed is more than 400MB/s
and this speed is the fastest among hardware
random number generators which utilize thermal
noise of electronic circuit for noise source. Resultsof statistical test are very excellent. We want to
say that users can use this equipment withoutworry. The other two hardware random number
generators have been introduced. Three generators are now available through our portal site.
Yoshiyasu Tamura

Research Centers䡵 Random number generator
We call methods to generate random numbers
random number generators. The random number
sequence generated by a computer is called
pseudo random numbers. On the other hand therandom number sequence generated by a physicalphenomenon is called physical random numbers.
In general pseudo random numbers has uniform
distribution on [0, 1). There are linear congruential method, M-sequence, Mersenne Twister andso on. The most popular phenomenon which isused for a hardware random number generator isthermal noise of electronic circuit. Recently
chaos phenomenon of semiconductor laser isused for a hardware random number generator.

computations are necessary to large-scale problems. Large numbers of random numbers arenecessary to large-scale computations. It seems
that we cannot do this computation by only one
CPU. Parallel computations can bring us someresults. There are many researches of methods forparallel pseudo random number generation. We,
however, recommend to use a hardware random
number generator for parallel computing.

ATX Power
Supply Cable
J103
Connector
Board #2
Board #1

Figure: Hardware Random Number Generation System

15

Research Cooperation

International Cooperation
䡵 Associated Foreign Research Institutes
Organization name

Address

Conclusion day

The Statistical Research Division of the U.S. Bureau of the Census

USA (Washington)

July 27, 1988 -Stichting Mathematisch Centrum

The Kingdom of the Netherland (Amsterdam)

May 10, 1989 -Statistical Research Center for Complex Systems, Seoul National University

The Republic of Korea (Seoul)

October 17, 2002 -Institute for Statistics and Econometrics, Humboldt University of Berlin

Germany (Berlin)

December 8, 2004 -Institute of Statistical Science, Academia Sinica

Taiwan (Taipei)

June 30, 2005 -The Steklov Mathematical InstituteRussia (Moscow)

August 9, 2005 -Central South University

China (Changsha)

November 18, 2005 -Soongsil University

The Republic of Korea (Seoul)

April 27, 2006 -Department of Statistics, University of Warwick

The United Kingdom (Coventry)

January 16, 2007 -The Indian Statistical Institute

India (Kolkata)

October 11, 2007 -Research Collaboration
This academic study program provides researchers from other academic institutes with access to thefacilities of the Institute, and provides opportunities for researchers to conduct theoretical and appliedstudies on statistics.

Research Cooperation

䡵 Number of Activities
2004

2005

2006

2007

2008

2009

108

124

122

120

138

154

䡵 Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below tofind the most appropriate type of project.
ISM Fields
Numbera

FieldsNumberFieldsSpatial and time series modelingfComputational statisticsbIntelligent information processinggMathematical statisticsc

Graph modelinghLearning and inferencedSurvey researchiComputational mathematicseMultidimensional data analysisj

OthersMajor Research Fields
Number16

FieldsMajor Research Domains

1

Statistical mathematics

Mathematical theory of statistics, optimization, etc.

2Information science

Algorithms, use of computer in statistics, etc.

3

Biological science

Medicine, pharmacy, epidemiology, genetics, etc.

4Physical science

Space, planet, earth, polar region, materials, etc.

5Engineering

Mechanics, electronics, control, chemistry, architecture, etc.

6

Human science

Philosophy, art, psychology, education, history, geography, culture, language, etc.

7Social science

Economics, law, politics, society, management, official statistics, population, etc.

8

OthersOther research fields

Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of the Graduate University for Advanced
Studies (the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate
education. Since its opening, the Institute has created the Department of Statistical Science and, since April
1989, has accepted students for education and research in doctoral programs. In 2006, the Institute adopted
a five-year system, offering either a five-year education and research program, or a three-year education andresearch program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Cyber Society and Culture
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science
Accelerator Science

President
School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics
Basic Biology

School of Life Science

Physiological Sciences

School of Advanced Sciences

Biosystems Science

Graduate School Program

Evolutionary Studies of Biosystems

Outline of Education and Research
The course includes modeling, forecasting, inference and designing of data-gathering systems in order
to extract information and knowledge from the real world based on empirical data, as well as basic, mathematical and applied education and research related to these fields. The course aims to provide thestudent with skills that help to contribute to solving important and connected issues and give the ability
to perform original research.
Field of Education and ResearchContents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information processing.
We also provide education and research on statistical inference based on various models, methods of calculation
for inference, and evaluation of models based on data.

Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Mathematical andStatistical Inference

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

17

Features of Education and Research The course is the only integrated doctoral program on statistical science in Japan. It has received
students from a wide variety of disciplines and has offered education and research on statisticalscience by professors specialized in many different fields, from theory through to practical applications.
 The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, aswell as a variety of software, including original statistical software developed by the Institute.
 The academic publications and books on statistical and mathematical sciences produced are some of
the best in the world.
 The library holds an extensive collection of books and journals, being one of the best in the world.
 In its role as an inter-university research institute, the Institute holds frequent workshops and seminars
by visiting professors and researchers from both Japan and abroad. Students are free to attend andparticipate.
 It is possible to collaborate with researchers from other universities and institutions. It is also possible
for students to develop their own projects by participating in research projects with other institutions
through the Transdisciplinary Research Integration Center, Research Organization of Information and
Systems.

Course Requirements and Type of Degree Granted
 Requirements to complete the course are as follows:
Completion of at least 40 credits while a graduate student of five years, or completion of at least 10
credits while a doctorate student of three years who graduated Master’s course meeting all the criteria
set by the thesis committee of the Institute and successfully completing the final examination.
 On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly withan inter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.
 The required number of years of study will be flexible if a student demonstrates outstanding research
results.

Graduate School Program

Number of Students ( As of April 1, 2010)* The figures in parentheses indicate the number of foreign students being supported at government expense.
* The figures in circles indicate those who are employed by other organizations.

 Doctor’s course three years
Year of enrollment

2002

2004

2005

2007

2008

2009

2010

Number of students

1

3

1 

4

6

5

3

Year of enrollment

2007

2008

2009

2010

Number of students22

1 (1 )

2 Doctor’s course five years

18Graduate School Program

University Background of Students
National and public universities• Hokkaido University • Tohoku University (2) • Fukushima University • University of Tsukuba (5) • Saitama University• Ochanomizu University • Hitotsubashi University (5) • Chiba University • The University of Tokyo (14) • Tokyo Gakugei University• Tokyo University of Agriculture and Technology • Tokyo Institute of Technology (2) • Tokyo University of Marine Science and Technology• Nagoya University (2) • Toyohashi University of Technology • Kyoto University (3) • Osaka University • Okayama University• Shimane University (2) • Kyushu University (2) • Oita University • Japan Advanced Institute of Science and Technology, Hokuriku• Osaka City University
Private universities• Keio University (4) • Chuo University (6) • Tokyo University of Science (5) • Toyo University • Nihon University (2)• Japan Women’s University • Hosei University (7) • Waseda University (5) • Nanzan University (1) • Kyoto Sangyo University• Okayama University of Science • Kurume University
Foreign universities• Aston University • Center for Analysis and Prediction, China Seismological Bureau• Chinese Academy of Sciences, Institute of Applied Mathematics • Jahangirnagar University (2) • Northeastern University, China• Ohio University • Stanford University • The Hong Kong University of Science and Technology • Universidade Eatadual of Campians• University of Colorado at Boulder (2) • University of Dhaka (2) • University of Hawaii • University of Malaya • University of Rahshahi• University of Science and Technology of China

Degrees AwardedYear

Doctor of Philosophy

Year

Doctor of Philosophy

1996
1997
1998
1999
2000
2001
2002

3 [1]
1
4 [1]

2003
2004
2005
2006
2007
2008
2009

8 [1]
4
4
8 [1]
7 [1]
4 [1]
5 [1]

6
5
5
4

* [ ] Ph.D. on the basis of the dissertation only (included in the total)

Current Position of Alumni ( As of April 1, 2010 )Graduate School Program

National and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine, Professor • University of Tsukuba, Professor (2)• University of Hyogo, Professor • Saitama University, Associate Professor • The University of Electro-Communications, Associate Professor• The University of Tokyo, Associate Professor • Kyushu University, Associate Professor • Kyushu Institute of Technology, Associate Professor• The Institute of Statistical Mathematics, Associate Professor • University of Tsukuba, Lecturer • Hokkaido University, Assistant• Chiba University, Assistant • Tokyo Institute of Technology, Assistant • Hiroshima University, Assistant• Kyushu University, Assistant Professor • University of the Ryukyus, Assistant • The Institute of Statistical Mathematics, Assistant Professor (5)• The University of Tokyo, Project Researcher • Tokyo Institute of Technology, Research Fellow• Nara Institute of Science and Technology, Research Fellow • The Institute of Statistical Mathematics, Project Researcher (10 )• Bank of Japan, Project Post • Japan Broadcasting Corporation • Financial Services Agency, Financial Research and Training Center, Researcher• The Institute of Statistical Mathematics, JST CREST Research Fellow • JST Basic Research Programs Doctoral Research Fellow• Railway Technical Research Institute, Senior Researcher • Statistical Information Institute for Consulting and Analysis• Government Pension Investment Fund • Public School, Teacher • The Institute of Statistical Mathematics, Research Fellow (2)Private universities• Sapporo Gakuin University, Professor • Meiji University, Professor • Doshisha University, Professor• Tokyo Health Care University, Associate Professor • Nihon University, Associate Professor • Tokyo University of Information Science, Lecturer• Josai University, Lecturer • Sapporo Gakuin University, Full-Time Lecturer • Tokyo Women’s Medical University, Postdoctoral Fellow
Foreign universities• Asia-Pacific Center for Security Studies Department, Associate Professor • Central South University, Professor• Hong Kong Baptist University, Lecturer • Jahangirnagar University, Professor • Jahangirnagar University, Associate Professor (2)• Massey University, Research Fellow • The University of Warwick, Research Fellow • University of Otaga, Research Fellow• University of Rajshahi, Associate Professor • University of South Carolina, Research Fellow • Victoria University, Senior Lecturer
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory, Research Fellow • NTT Communication Science Laboratories, Research Fellow• Seiwa Kikaku • Toyota Motor Corporation, Higashi-Fuji Technical Center, Research Fellow • NLI Research Institute, Chief Researcher• Sankyo Co., LTD. • Mizuho Trust and Banking, Senior Researcher (2) • JP Morgan Trust Bank Limited, Vice President (Hosei University, Part-Time Lecturer)• ATR Computational Neuroscience Laboratories, Research Fellow • Schlumberger Limited • Macquarie Securities, Japan, Quantitative Analyst• Non-Life Insurance Rating Organization of Japan, Staff Member • Open Technologies Corporation • Yamaha Corporation

19

Outreach Activities

Tutorial Courses
䡵 History
The statistical education program started in 1944, the year that the Institute of Statistical Mathematics
was founded, as an education program at the Numerical Computation Training Center of the Science
Research Technical Assistant Training Center of the Ministry of Education, located in the Institute. In
1947, the affiliated Statistical Technician Training Center was opened as an educational organization for
statistical technicians and instructors, in order to improve the levels of staff training within the statisticalorganizations of the government and to supply statisticians.
As social needs have changed, the purpose of the education program has gradually shifted away fromthe initial aim of supplying well qualified statistical technicians for the government, towards statisticaleducation for working people. Tutorial courses were therefore initiated. Later, statistical methods became
more widely used across a broad range of fields. Consequently, more extensive and sophisticated statisticaleducation was required. To meet this demand, tutorial courses were expanded to cover a wider range ofstatistical topics. From 1965 to 1985, six to eight courses were held annually, not only in Tokyo, but also inprovincial cities such as Osaka, Okayama and Fukuoka.
In 1985, the Institute was reorganized as a member of the Inter-University Research Institute Corporation,and the affiliated Statistical Technician Training Center was abolished. However, in response to consistentpublic demand for tutorial courses, the Center for Engineering and Technical Support, together withother departments, ran three to four courses annually. In 2005, the number of courses rose to 13. In 2009,
the number of courses was 13.

䡵 Courses
The total number of courses held from 1969 to March, 2010 was 259, with a total of 19,138 participants.
These courses covered a wide range of fields from basic to applied statistics. The following table lists thecourses held in the past 3 years:

Outreach Activities

Year

Category

Title

Month

2007

Basic course

Introduction to Risk Analysis with R— Application of Tree-based and Nonparametric Modelling —January

49

Basic course

Introduction to Survey Data Analysis Using R

February

40

Basic course

Introduction to Quantitative Methods for Social Sciences

May-July

54

Basic course

Introduction to Sampling Methods and Sample Surveys

June

50

Basic course

Elementary Course on Time Series Analysis

July

61

Basic course

Introduction to Statistics

July

70

Standard course

Statistics of Extremes

September

61

Basic course

Introduction to Statistical Data Analysis Focused on Multivariate Analysis

October47

Basic course

Quantification Methods for Qualitative Data

October-December 46

Basic course

Recent Topics on International Standardization of Statistical Methods— Standardization of Sampling Inspection and Statistical Process Management —November

11

Basic course

Statistical Quality Control

November

16Standard course

Taguchi Methods for Robust Design

November

28Standard course

Geometrical Structures Underlying Information Quantities:
Mathematics of Kullback-Leibler Information

December52

Basic course

Statistical Causal Analysis by Structural Equation Modelling

February

58

Standard course

Regression Models for Count Data and Their Extension

February

63

Basic course

Data Analysis and Simulation with R

May

69

Basic course

The Grammar of Science and Descriptive Statistical Methods

June

47

Basic course

Introduction to Statistics

July

79

Standard course

Spatial Point Pattern Analysis — Introduction from Biological Examples

August

46

2008

20

Number of participantsTutorial Courses
Year

Category

Title

Month

2008

Standard course

Statistics of Extremes

August48

Standard course

An Introduction to Statistical Analysis by the Theory of Martingales

September48

Basic course

Analysis of Sample Surveys with R

September

41

Basic course

Introduction to Multivariate Analysis

October

71

2009

2010

Number of participantsStandard course

Data Assimilation: State Space Model and Simulation

October72

Basic course

Quality Control, Quality Engineering (Taguchi Method)

November17

Basic course

Introduction to Pharmacoepidemiology

November

71Standard course

Understanding of Evolutionary Computation: Statistical Viewpoint

December

56

Standard course

Markov Chain Monte Carlo : Basics and Examples

February

78

Standard course

Data Assimilation Methodology in Practice

April

77

Basic course

Introduction to Sampling Methods and Survey Data Analysis

May

71

Standard course

Introduction to Multivariate Descriptive Data Analysis by R

May

47

Basic course

Statistical Data Visualization

June

75

Basic course

Introduction to Pharmacoepidemiology

June

73

Basic course

Data Analysis for Marketing

June-July74

Basic course

Akaike Information Criterion and Statistical Modeling:
Introduction from Field Biological Data

July

73

Basic course

Introduction to Statistics

July

78

Standard course

Regularization and Estimation— LASSO and Related Topics, Basic Theory and Applications —August44

Basic-Standard course

Elementary of Multivariate Analysis

August76

Basic course

Statistics Literacy for the Law Court — For the Base on the Rational Discussion —November36

Basic course

Introduction to Text Mining with R

November

51

Standard course

Markov Chain Monte Carlo : Basics and Examples / 2010

February

97

The schedule of tutorial courses can be found on the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/lectures/kouza.html

Statistical Mathematics Seminar
The Institute holds a one-hour seminar every Wednesday to showcase the latest studies of our academic
staff and guest researchers. The seminar is open to the public. For details, please visit the website of theInstitute.
http://www.ism.ac.jp/

Outreach Activities

Open Lecture
We hold an open lecture during Education and Culture Week every year (November 1 through 7), tointroduce the Institute’s activities and to promote statistical science. We invite lecturers to speak on atimely topic relating to statistical science. The lecture is open to the general public. For further information, please visit the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/21Outreach Activities

Consultation about Statistical Science
The Institute provides a consultation service on statistical science for the general public and researchers
as a means of actively sharing the benefits of our research with society. This service operates throughout
the year. Please contact the Center for Engineering and Technical Support. The consultation service covers
a variety of topic ranging from the basics of statistics to more specialized issues. Half of the advice receivers
are from the private sector, and the rest are staff from public organizations, university teachers, andstudents. Each teacher at the Institute directly takes charge of about 20 specialized cases annually, someof which are publicized at academic conferences, as being of benefit to society.

Annual Research Meeting for 2009
The Annual Research Meeting of the Institute was held on March 18 and 19, 2010. This annual meeting is
an opportunity for the Institute’s researchers and visiting researchers to present the results of their studies
from the previous year. The meeting has been held every year since the foundation of the Institute in 1944.
In the early years, as there were few staff members and participants, they were able to have enthusiastic
discussions all day long. However, as the number of staff members has increased, the meeting is now held
over two days and each presenter is given 12 minutes to report on the results of his/her own research. A
wide variety of topics is covered, ranging from statistical contributions to issues facing today’s society tomore fundamental studies. This year’s meeting started with an opening address by Junji Nakano, andgreetings from Genshiro Kitagawa. A total of 49 research education staff and 8 visiting researchers then gave
their presentations. In addition, a poster session was held by 47 research fellows and graduate students
of Sokendai. From 2004, we have begun to compile and distribute proceedings in advance. We also host
visitors from other organizations. The program for this meeting is available from the website.
http://www.ism.ac.jp/

Outreach Activities
22Facilities and Equipment
Facilities and Equipment

Computation Resources ( As of October 1, 2010)
Since January 2010, the Supercomputer System for Statistical Science has been in operation and has
analyzed a large volume of statistical data. The main components are a shared memory system (Fujitsu
SPARC Enterprise M9000 with 2 nodes, the first with 64 quad-core SPARC64 CPUs and 2 TB of main
memory, and the second with 24 quad-core SPARC64 CPUs and 1 TB of main memory), and a distributed
memory system (Fujitsu PRIMERGY RX200S5 with 360 nodes, each with 2 quad-core Xeon X5570 CPUs
and 48 or 24 GB of main memory. In total, 2880 cores and 12.1 TB of memory are available). The system
also includes a large-scale shared storage system (1.37 PB disk storage supported by RAID6), a physicalrandom number generating system (two random number generator boards, each of which can generaterandom numbers at 400 MB/s) and a visualization system (including a SXRD projector with a maximum
resolution of 4,096 × 2,160 and a 200-inch rear projection screen).
In the new office building, the primary Local Area Network (LAN) consists of an Ethernet network using
10GBASE-SR for the main trunk and 1000Base-T for branches. The personal computers in researchers’
offices, the Supercomputer System for Statistical
Science are all connected to this network. A wireless
LAN system that supports IEEE 802.11a,b,g,n is alsoavailable in the area of the building occupied by theinstitute. These LAN systems enable distributed
processing and computing resources and statistical
data to be used effectively. Comprehensive network
security measures have been implemented such as afirewall system, anti-virus software, and an intrusion
prevention system. To encourage joint researchwith researchers both in Japan and abroad, as well as
the exchange of e-mails, the network is connectedto the Internet through SINET3 (1 Gbps).
Supercomputer System for Statistical Science

Library and Materials ( As of April 1, 2010)
We have a large number of major Japanese/foreign journals covering a wide variety of fields including
statistics, mathematics, computer science and informatics. In addition, we also have a large libraryconsisting of books on humanities, social science, biology, medical science, science and engineering.
Besides contributed to Japanese and foreign publications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Statistical Mathematics (English; Springer), Proceedings of theInstitute of Statistical Mathematics (Japanese), Research Report (Statistical Researches mainly related to
the Japanese National Character), Computer Science Monographs, Cooperative Research Reports (forcollaborative research projects), Research Memorandum, ISM Reports on Statistical Computing, and ISM
Report on Research and Education.
All materials are properly catalogued
JapaneseJapanese Foreign
and can be searched from the web in
books
periodicals periodicals
order to meet the needs of researchers
15,595working in a wide of fields. We also accept
985
27%
Books
Periodicals 1,155
photocopy requests.
46%
54%
57,8382,140

Foreign books
42,24373%

23

Finance and Buildings

Administration Subsidy and Others ( 2009 )Type

Personnel expenses

Non-personnel expenses

Total

862,269

1,120,568

1,982,837

Expenditure

Finance and Buildings

Unit: ¥1,000

Accepted External Funds ( 2009 )Type

Subcontracted
research

Joint researchContribution for
scholarship

Total

Items

15

1

5

21

Income170,060

5,000

3,900

178,960
Unit: ¥1,000

Grant-in-Aid for Scientific Research ( 2009 )Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research on Priority Areas

1

1,600

Grant-in-Aid for Scientific Research ( A )544,330Grant-in-Aid for Scientific Research ( B)5

21,710Grant-in-Aid for Scientific Research (C)13

17,160Grant-in-Aid for Young Scientists ( A )

1

5,460

Grant-in-Aid for Young Scientists ( B)1112,480Grant-in-Aid for Young Scientists (Start-up)

1

1,560

Grant-in-Aid for JSPS Fellows

1

1,100

38

105,400

1

5,980

Total
Grant-in-Aid for Scientific Research, Ministry of Health, Labour and Welfare

Unit: ¥1,000

Site and Buildings ( As of June 1, 2010 )24

Site Area

62,450m 2

Area for Buildings ( total )16,026m 2

Organization

Organization Diagram ( As off August 1, 2010 )䡵 Spatial and Time Series Modeling Group

Cooperative
Research
Committee

Department of Statistical Modeling䡵 Graph Modeling Group䡵 Survey Research Group

Department of Data Science䡵 Multidimensional Data Analysis Group䡵 Computational Statistics GroupCouncil

Scientific
Advisory
Board䡵 Intelligent Information Processing Group

Department of Mathematical
Analysis and Statistical Inference䡵 Mathematical Statistics Group䡵 Learning and Inference Group䡵 Computational Mathematics Group䡵 Molecular Evolution Research Group

Prediction and Knowledge Discovery
Research Center (PKDRC)

䡵 Data Assimilation Research Group䡵 Statistical Seismology Research Group䡵 Statistical Genome Diversity Research Group

Organization

䡵 Food and Drug Safety Research Group

DirectorGeneral

Risk Analysis Research Center( RARC )䡵 Environmental Risk Research Group䡵 Financial Risk and Insurance Research Group䡵 Research Group for Reliability andQuality Assurance of Service and Product䡵 Social Survey Information Research Group

Vice DirectorGeneral䡵 Functional Analytic Inference Research Group

Research Innovation Center䡵 Advanced Monte Carlo Algorithm Research Group䡵 Random Number Research Group䡵 Speech and Music Information Research Group䡵 Optimization-based Inference Research Group

Center for Engineering andTechnical Support

䡵 Computing Facilities Unit䡵 Information Resources Unit䡵 Media Development Unit

Library

Planning Unit
Evaluation Unit
Administration Planningand Coordination Section

Information and Public Relations UnitIntellectual Property Unit
NOE Forwarding Unit

National Institute ofPolar Research

Planning Section ( NIPR )NIPR / ISM Joint Administration
OfficePlanning Section ( ISM )The Institute of
Statistical Mahtematics

General Service Center25

Number of Staff ( As of August 1, 2010 )Director- Professor Associate
GeneralProfessor

Type

Director-General

Assistant Administrative Technical
Professor
Staff
Staff

1

Total

1

Department of Statistical Modeling

4

7

5

16

Department of Data Science

8

6

3

17

Department of Mathematical Analysis and Statistical Inference

4

5

6

15

Center for Engineering and Technical Support

99Administration Planning and Cordination Section

1

NIPR / ISM Joint Administration Office

12 (26)1 (2)

13 (28)13 (26)

10 (2)

72 (28)

Total

1

16

18
(

14

1

) Total Number of Staff of NIPR / ISM Joint Administration Office

Organization

Staff ( As of August 1, 2010 )Director-General

Genshiro KITAGAWA

Vice Director-General

Takashi NAKAMURA

Vice Director-General

Hiroe TSUBAKI

Vice Director-General

Tomoko MATSUI

Department of Statistical Modeling
Director

Tomoyuki HIGUCHI䡵 Spatial and Time Series Modeling Group
Prof.
Prof.
Assoc. Prof.
Assoc. Prof.

Yosihiko OGATA
Tomoyuki HIGUCHI
Yoshinori KAWASAKI
Kenichiro SHIMATANI

Genta UENO
Fumikazu MIWAKEICHI
Ryo YOSHIDA
Jiancang ZHUANG

Assist. Prof.

Yukito IBA
Yumi TAKIZAWA

Assist. Prof.

Assoc. Prof.

Visiting Prof.

Hiroshi SOMEYAMasataka GOTO

Assist. Prof.

Ying CAO

Adjunct Prof.

Masami HASEGAWA

Assoc. Prof.
Assoc. Prof.
Assist. Prof.
Assist. Prof.

Project Researcher
Project Researcher

Shinya NAKANO
Toshihiro ABE
Ken ISHIKAWA

䡵 Intelligent Information Processing Group
Prof.
Prof.

Tomoko MATSUI
Kenji FUKUMIZU

Assoc. Prof.

䡵 Graph Modeling Group
Assoc. Prof.

Jun ADACHI

Department of Data Science
Director

Yoshiyasu TAMURA䡵 Survey Research Group
Prof.
Prof.

Takashi NAKAMURA
Ryozo YOSHINO

Assoc. Prof.
Assoc. Prof.
Assist. Prof.

Tadahiko MAEDA
Takahiro TSUCHIYA
Koken OZAKI

Visiting Assoc. Prof. Wataru MATSUMOTO

Project Researcher

Kiyohisa SHIBAIJun YAMASHITA

Shigeyuki MATSUI
Satoshi YAMASHITA
Toshihiko KAWAMURA

Project Researcher

Hei CHAN

Project Researcher䡵 Multidimensional Data Analysis Group
Prof.
Prof.
Prof.

26

Toshiharu FUJITA
Hiroe TSUBAKI
Nobuhisa KASHIWAGIProf.
Assoc. Prof.
Assist. Prof.

Organization

Staff
䡵 Computational Statistics Group
Prof.
Prof.
Assoc. Prof.
Assoc. Prof.

Yoshiyasu TAMURA
Junji NAKANO
Naomasa MARUYAMA
Koji KANEFUJI

Assoc. Prof.
Assist. Prof.
Visiting Prof.
Visiting Prof.

Seisho SATO
Nobuo SHIMIZU
Yuichi MORI
Kazunori YAMAGUCHI

Visiting Assoc. Prof. Norikazu IKOMA
Project Researcher

Hayato TAKAHASHI

Department of Mathematical Analysis and Statistical Inference
Director

Shinto EGUCHI䡵 Mathematical Statistics Group
Prof.
Assoc. Prof.

Satoshi KURIKI
Yoichi NISHIYAMA

Shuhei MANO
Takaaki SHIMURA

Assist. Prof.

Hironori FUJISAWA
Tadayoshi FUSHIKI

Assist. Prof.

Assist. Prof.

Assist. Prof.

Masayuki HENMI
Shinsuke KOYAMAProf.

Atsushi YOSHIMOTO

Assoc. Prof.

Satoshi ITOAssist. Prof.
Assist. Prof.

Assist. Prof.

Kei KOBAYASHI
Shogo KATO

䡵 Learning and Inference Group
Assoc. Prof.

Shinto EGUCHI
Shiro IKEDA

Assoc. Prof.

Organization

Prof.

䡵 Computational Mathematics GroupProf.

Yoshihiko MIYASATO

Prediction and Knowledge Discovery Research Center (PKDRC)
Director

Shinto EGUCHI䡵 Molecular Evolution Research Group
Assoc. Prof.

Jun ADACHIAssist. Prof.

Ying CAO

Assist. Prof.

Ryo YOSHIDA
Shinya NAKANO

䡵 Data Assimilation Research Group
Prof.
Assoc. Prof.

Tomoyuki HIGUCHI
Genta UENO

Assist. Prof.

Project Researcher
Project Researcher
Project Researcher䡵 Statistical Seismology Research GroupProf.

Yosihiko OGATA

Visiting Prof.

Shinji TODA

Project Researcher

Assist. Prof.

Jiancang ZHUANG

Project Professor

Mitsuhiro MATSU’URA

Project Researcher

Hironori FUJISAWA
Tadayoshi FUSHIKI
Shogo KATO

Visiting Prof.

Tomoko IMOTO
Masaya SAITO
Hiromichi NAGAO
Ann Anning CHU
Katsunori SUGAYA䡵 Statistical Genome Diversity Research Group
Prof.
Prof.
Assoc. Prof.

Shinto EGUCHI
Satoshi KURIKI
Shiro IKEDA

Assoc. Prof.
Assist. Prof.
Assist. Prof.

Project Researcher

Masaaki MATSUURA
Osamu KOMORI

Risk Analysis Research Center (RARC)
Director

Hiroe TSUBAKI

Coordinator

Koji KANEFUJI

Vice Director / Coordinator

Toshiharu FUJITA

Coordinator

Yoshinori KAWASAKI

Coordinator

Shigeyuki MATSUI

Coordinator

Toshihiko KAWAMURA䡵 Food and Drug Safety Research Group
Prof.
Prof.
Prof.
Assist. Prof.
Assist. Prof.

Toshiharu FUJITA
Hiroe TSUBAKI
Shigeyuki MATSUI
Takaaki SHIMURA
Masayuki HENMI

Visiting Prof.

Visiting Assoc. Prof. Makoto TOMITA

Visiting Prof.

Manabu IWASAKI
Toshiya SATO
Visiting Prof.
Yoshimitsu HIEJIMA
Yoichi KATO
Visiting Prof.
Visiting Assoc. Prof. Toshio OHNISHI

Visiting Assoc. Prof. Satoshi AOKI
Project Assist. Prof.
Project Researcher

Takafumi KUBOTAYosuke FUJII

27

Risk Analysis Research Center

䡵 Environmental Risk Research Group
Prof.
Prof.
Assoc. Prof.
Visiting Prof.
Visiting Prof.

Nobuhisa KASHIWAGI
Atsushi YOSHIMOTO
Koji KANEFUJI
Yukio MATSUMOTO
Kunio SHIMIZU

Visiting Prof.

Visiting Assoc. Prof. Koji OKUHARA

Visiting Prof.

Hidetoshi KONNO
Kazuo YAMAMOTO
Yoshiro ONO
Visiting Prof.
Mihoko MINAMI
Visiting Prof.
Visiting Assoc. Prof. Hirokazu TAKANASHI

Visiting Assoc. Prof. Tomohiro TASAKI
Project Researcher
Project Researcher
Project Researcher

Masaki OKUDA
Masayuki KAGEYAMA
Takayuki FUJII䡵 Financial Risk and Insurance Research Group
Assoc. Prof.
Assoc. Prof.
Assoc. Prof.
Assoc. Prof.

Satoshi YAMASHITA
Seisho SATO
Yoshinori KAWASAKI
Yoichi NISHIYAMA

Visiting Prof.

Visiting Assoc. Prof. Masakazu ANDO

Visiting Prof.

Project Researcher

Naoto KUNITOMO
Hiroshi TSUDA
Michiko MIYAMOTO
Visiting Prof.
Visiting Assoc. Prof. Toshinao YOSHIBA

Kentaro AKASHI

Project Researcher

Christopher Andrew ZAPART

Project Researcher

Yoko TANOKURA

䡵 Research Group for Reliability and Quality Assurance of Service and Product

Organization

Prof.
Assist. Prof.
Visiting Prof.
Visiting Prof.

Hiroe TSUBAKI
Toshihiko KAWAMURA
Kakuro AMASAKA
Kazuo TATEBAYASHI

Visiting Prof.

Visiting Assoc. Prof. Takahiro HOSHINO

Visiting Prof.

Sadaaki MIYAMOTO
Shusaku TSUMOTO
Visiting Assoc. Prof. Manabu KUROKI

Visiting Assoc. Prof. Yukihiko OKADA
Visiting Assoc. Prof. Hideki KATAGIRI

Research Innovation Center
Director

Kenji FUKUMIZU

䡵 Social Survey Information Research Group
Prof.
Prof.

Takashi NAKAMURA
Ryozo YOSHINO

Assoc. Prof.
Assoc. Prof.

Tadahiko MAEDA
Takahiro TSUCHIYA

Visiting Assoc. Prof. Toru KIKKAWA

䡵 Functional Analytic Inference Research GroupProf.

Kenji FUKUMIZU

Assist. Prof.

Kei KOBAYASHI䡵 Advanced Monte Carlo Algorithm Research Group
Assoc. Prof.

Yukito IBA

Visiting Prof.

Makoto KIKUCHI

Visiting Assoc. Prof.

Toru ONODERA

Visiting Assoc. Prof.

Koji HUKUSHIMA

Assoc. Prof.

Genta UENO

Project Researcher

Keisuke HAYASHI
Hitoshi MOTOYAMA䡵 Random Number Research GroupProf.

Yoshiyasu TAMURA

䡵 Speech and Music Information Research GroupProf.

Tomoko MATSUIAssist. Prof.

Shinsuke KOYAMA䡵 Optimization-based Inference Research Group
Assoc. Prof.

Satoshi ITO

Assoc. Prof.

Shiro IKEDA

Project Researchers
Project Professor

Ryozo MIURA

Project Researcher Motoi OKAMOTO
Project Researcher Satoko SAITA

28

Project Researcher
Project Researcher

Eiki TANAKA
Xiaoling DOU

Project Researcher

Organization

Staff
Center for Engineering and Technical Support
Director

Junji NAKANO

Senior Specialist

Saeko TANAKA

Vice Director

Satoshi YAMASHITA

Adjunct Prof.

Yasumasa BABA

Deputy Manager

Yuriko WATANABE

Adjunct Prof.

Makio ISHIGURO

Head of Computing Facilitise Unit
Head of Information Resources Unit (Concurrent )Head of Media Development Unit (Concurrent )Kazuhiro NAKAMURA
Saeko TANAKA
Yuriko WATANABE

Visiting Prof.
Visiting Prof.

Michiko WATANABE
Yoshihiko KONNO

Library
Junji NAKANOHeadOrganization

Administration Planning and Coordination Section
Director
Head of Planning Unit
Head of Evaluation Unit
Head of Information and Public Relations Unit

Genshiro KITAGAWA

Hiroe TSUBAKI
Takashi NAKAMURA
Tomoko MATSUIHead of Intellectual Property Unit
Head of NOE Forwarding Unit
Visiting Prof.

Tomoko MATSUIGenshiro KITAGAWA
Takashi NAMESHIDANIPR / ISM Joint Administration OfficeDirector

Masaru SHIMODA

Director of General Service Center

Tsugio TOKUTA

䡵 Planning Section ( ISM )Head of Planning Section
Deputy Head
Specialist

Hiroshi YAMAMOTO
Fumio SUTOTeam Leader
Team Leader

Yoshiki HAMA

Motoyoshi URANO
Hiroaki ARAI

Team Leader

Akihiko NAKAMURA

䡵 Planning Section ( NIPR )Head of Planning Section Tsuyoshi ABE
Deputy Head

Yasuyuki EDURETeam Leader

Motokazu TOYODATeam Leader

Nobutaka IRIETeam Leader

Hiroshi YOSHIOKA
Tomoe HIRANUMA
Kazuyoshi ASO

Team Leader

Yoji ISHII
Tomoko IIDA
Kuniyasu YOKOI䡵 General Service Center
Deputy Head
Deputy Head
Deputy Head

Takashi KOSAKA
Koji SAKAMOTO
Toru HAYASHITeam Leader
Team Leader

Team Leader
Team Leader

29

Organization

Council of The Institute of Statistical Mathematics ( As of April 1, 2010)
Masanori IYEYutaka KANO
Toshinari KAMAKURA
Koji KURIHARA
Kunio SHIMIZU
Kazuo SEIYAMA
Makoto TAIJI

Organization

Nobuhiko TERUIYoshihiro YAJIMA
Takashi WASHIO
Takashi NAKAMURA
Hiroe TSUBAKI
Tomoko MATSUI
Tomoyuki HIGUCHI
Yoshiyasu TAMURA
Shinto EGUCHI
Junji NAKANO
Satoshi KURIKI
Kenji FUKUMIZU
Yosihiko OGATA
Nobuhisa KASHIWAGI

Professor, Optical and Infrared Astronomy Division, National Astronomical Observatory of Japan,
National Institutes of Natural Sciences
Professor, Graduate School of Engineering Science, Osaka University
Professor, Faculty of Science and Engineering, Chuo University
Professor, Graduate School of Environmental Science, Okayama University
Professor, Faculty of Science and Technology, Keio University
Professor, Graduate School of Humanities and Sociology, the University of Tokyo
Group Director, Computational Systems Biology Research Group,
Advanced Computational Sciences Department, Advanced Science Institute, RIKEN
Professor, Graduate School of Economics and Management, Tohoku University
Professor, Graduate School of Economics, The University of Tokyo
Professor, the Institute of Scientific and Industrial Research, Osaka University
Professor ( Vice Director-General, ISM)
Professor ( Vice Director-General, ISM)
Professor ( Vice Director-General, ISM)
Professor ( Director of Department of Statistical Modeling, ISM)
Professor ( Director of Department of Data Science, ISM)
Professor ( Director of Department of Mathematical Analysis and Statistical Inference, ISM)
Professor ( Director of Center for Engineering and Technical Support, ISM)
Professor ( Department of Mathematical Analysis and Statistical Inference, ISM)
Professor ( Department of Statistical Modeling, ISM)
Professor ( Department of Statistical Modeling, ISM)
Professor ( Department of Data Science, ISM )Cooperative Research Committee ( As of April 1, 2010 )Akifumi OIKAWA
Hidetoshi KONNO
Toshimitsu HAMASAKI
Michiko WATANABE
Yuichi MORI
Ryozo YOSHINO
Shinto EGUCHI
Nobuhisa KASHIWAGI
Atsushi YOSHIMOTO

Professor and Presidential Aide, The Graduate University for Advanced Studies
Professor, Graduate School of System and Information Engineering, University of Tsukuba
Associate Professor, Graduate School of Medicine, University of Osaka
Professor, Faculty of Economics, Toyo University
Professor, Faculty of Informatics, Okayama University of Science
Professor ( Department of Data Science, ISM)
Professor ( Department of Mathematical Analysis and Statistical Inference, ISM)
Professor ( Department of Data Science, ISM)
Professor ( Department of Mathematical Analysis and Statistical Inference, ISM)

Research Ethics Review Committee ( As of April 1, 2010)
Specialist on epidemiology and social researchSpecialist on epidemiology and social research
Person in citizens’ position
Specialist in the field of ethics and law
Research education staff of ISM
Research education staff of ISM
Research education staff of ISM
Research education staff of ISM

Michio UMINO
Yoshihiko MIURA
Yutaka KURIKI
Hitomi NAKAYAMA
Takashi NAKAMURA
Hiroe TSUBAKI
Shigeyuki MATSUITadahiko MAEDA

Professor Extraordinary, Tohoku University
Professor, School of Health and Social Service, Saitama Prefectural University
Principal, Tachikawa City Daiichi Elementary School
Lawyer, Kasumigaseki-Sogo Law Offices
Professor ( Department of Data Science, ISM )Professor ( Department of Data Science, ISM )Professor ( Department of Data Science, ISM )Associate Professor ( Department of Data Science, ISM )Professor Emeritus ( As of August 1, 2010)
Kameo MATSUSHITA
Shigeki NISHIHIRA
Tatsuzo SUZUKI
Giichiro SUZUKI
Ryoichi SHIMIZU

30

Noboru OHSUMI
Masakatsu MURAKAMI
Kunio TANABE
Tadashi MATSUNAWA
Masami HASEGAWA

Yoshiyuki SAKAMOTO
Takemi YANAGIMOTO
Yoshiaki ITOH
Yasumasa BABA
Katsuomi HIRANO

Masaharu TANEMURA
Makio ISHIGUROHistory

1944

June 䢇

Based on a proposal submitted at an academic study conference in December 1943, the organization
was founded as an institute under the direct control of the Ministry of Education. This proposal aimed toprovide supervision for studies looking into the mathematical principles of probability and theirapplication, and was also intended to facilitate, unify and promote the publication of research results.

1947

April 䢇The affiliated statistical specialists’ school was opened.

May 䢇

The Institute was divided into the 1st Research Dept. (fundamental theories), the 2nd Research Dept. (statisticaltheories for the natural sciences), and the 3rd Research Dept. (statistical theories for the social sciences).

June 䢇The Institute was placed under the control of the Ministry of Education because of the enforcement of
the Ministry of Education Establishment Law.

1955 September 䢇

Reorganized into the 1st Research Dept. (fundamental theories), the 2nd Research Dept. (natural andsocial science theories), and the 3rd Research Dept. (operations, research, statistical analysis theories).
The laboratory system, comprising 9 laboratories and the research guidance promotion room, was adopted.

1949

1969

October 䢇1971

April 䢇The 4th Research Dept. (informatics theories) was instituted.

1973

April 䢇The 5th Research Dept. (prediction and control theories) was instituted.

1975

October 䢇1979 November 䢇

A new office building was constructed.

The 6th Research Dept. (statistical theories of human behavior) was instituted.
The Information Research Building was constructed.

April 䢇Reorganized as an Inter-University Research Institute owing to the revision of the Order for the Enforcement of the National School Establishment Law. The revised law required that the Institute would, as
an National Inter-University Research Institute, 1) conduct studies on statistical mathematics and its
application, 2) provide opportunities for university teachers or other researchers majoring in this field
to utilize the facility, and 3) contribute to the development of academic studies in universities.
At the same time, the 6 research departments were reorganized into 4 research departments(Fundamental Statistical Theory, Statistical Methodology, Prediction and Control, and Interdisciplinary
Statistics). The Statistical Data Analysis Center and the Statistical Education and Information Center
were instituted, and the affiliated Statistical Technician Training Center was abolished.

1988

October 䢇

The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science,part of the Graduate University for Advanced Studies ( SOKENDAI ).

1999

June 䢇The Institute was reorganized as an Inter-University Research Institute based on the National School
Establishment Law.

1993

April 䢇The Planning Coordination Chief System was instituted.

1997

April 䢇The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of
Statistical Computing, and the Statistical Education and Information Center was reorganized into theCenter for Information on Statistical Sciences.

2003 September 䢇

History

1985

The Prediction and Knowledge Discovery Research Center was instituted in the affiliated facility.

2004

April 䢇The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research
Organization of Information and Systems of the Inter-University Research Institute based on the
National University Corporation Law. The Planning Coordination Chief System was abolished and theposition of Vice Director-General was instituted instead. The Dept. of Statistical Science in the Schoolof Mathematical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical
Science and the School of Multidisciplinary Sciences were instituted.

2005

April 䢇The research organization was reorganized into three research departments (the Department of Statistical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical
Inference). The affiliated Center for Development of Statistical Computing, the Center for Information on
Statistical Sciences, and the Engineering and Technical Services Section were integrated into the Center
for Engineering and Technical Support. The affiliated facilities were reorganized as research departments,
and the Risk Analysis Research Center was instituted.

2008

April 䢇The Research Innovation Center was instituted in the affiliated facility.
The Intellectual Property Unit was instituted.

2009

October 䢇2010June 䢇

Officially opened the Akaike Guest House.

July 䢇

Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch
the General Service Center.

The ISM was moved to 10-3 Midori-cho, Tachikawa, Tokyo.

The NOE Forwarding Unit was instituted within the Administration Planning and Coordination Section.

31

The Institute of Statistical Mathematics
Research Organization of Information and Systems

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Tel: +81-(0 ) 50-5533-8500
Fax: +81- ( 0 ) 42-527-9302
http://www.ism.ac.jp/

The Institute of
Statistical Mathematics(

Bus stop
TachikawaShiyakusho

to Kamikitadai

Tachikawa City Office)

Bus stop (Saibansho-mae)
Judicial
CourtsTakamatsu

Local Autonomy
College

National Institutefor Japanese Language
and Linguistics

Tachikawa
Police Station
Disaster
MedicalCenterTama
MonorailShowa Kinen Park

Ome Line

Tachikawa-kita

Tachikawa
Access to the ISM

Chuo Lineto Shinjuku,
Tokyo →

Tama Monorailca. 10 min walk from Takamatsu Sta.
Tachikawa Bus
ca. 5 min walk from Saibansho-mae
or Tachikawa-Shiyakusho bus stop

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Tel: +81- ( 0 ) 50-5533-8500 Fax: +81- ( 0 ) 42-527-9302

http://www.ism.ac.jp/